Instructions to use jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit")
model = AutoModelForCausalLM.from_pretrained("jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit

SGLang

How to use jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit with Docker Model Runner:
```
docker model run hf.co/jhon53/Llama3_1_8B_Finance_QLoRA-merged-16bit
```

Llama-3.1-8B Financial Sentiment — QLoRA

LoRA adapters fine-tuned on FinGPT/fingpt-sentiment-train with QLoRA: 4-bit NF4 frozen base + bf16 LoRA adapters (r=16, alpha=32, dropout=0.05).

Task: 3-class financial sentiment — negative / neutral / positive

Evaluation Results

Dataset	Base Accuracy	FT Accuracy	Base Macro-F1	FT Macro-F1
FPB in-domain	0.8908	0.9748	0.8765	0.9725
FiQA-SA OOD	0.8120	0.9402	0.6705	0.8335

Baseline = zero-shot Meta-Llama-3.1-8B-Instruct with the same prompt template.

Quick Start

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Meta-Llama-3.1-8B-Instruct",
    load_in_4bit=True,
    device_map="auto",
)
model     = PeftModel.from_pretrained(base, "jhon53/Llama3_1_8B_Finance_QLoRA")
tokenizer = AutoTokenizer.from_pretrained("jhon53/Llama3_1_8B_Finance_QLoRA")

Training Details

Param	Value
Base model	meta-llama/Meta-Llama-3.1-8B-Instruct
Method	QLoRA (4-bit NF4 + LoRA bf16)
LoRA rank (r)	16
LoRA alpha	32
LoRA dropout	0.05
Target modules	q, k, v, o, gate, up, down projections
Training data	FinGPT/fingpt-sentiment-train (~76k)
Optimizer	AdamW 8-bit
Learning rate	2e-4 (cosine schedule)
Epochs	3 (early stopping, patience=3)
Loss	Completion-only cross-entropy